chore: import upstream snapshot with attribution

2026-07-13 12:38:16 +08:00
commit 94057c3d3e
7152 changed files with 2120455 additions and 0 deletions
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+# Copyright 2026 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Torch fallback for token filter operations (non-CUDA devices and HIP).
+
+Sets or clears specific bits in an int32 bitmask by token ID.  The token list
+is typically tiny (< 10 entries); aggregation is done in Python with the actual
+bitmask operations using torch tensor indexing.
+"""
+
+import ctypes
+from typing import List
+
+import torch
+
+
+def set_token_filter_torch(
+    vocab_mask: torch.Tensor,
+    token_ids: List[int],
+    batch_idx: int,
+    is_allowed: bool = True,
+    reset_vocab_mask: bool = True,
+):
+    if reset_vocab_mask:
+        vocab_mask[batch_idx].fill_(-1 if (not is_allowed) else 0)
+
+    if not token_ids:
+        return
+
+    # Aggregate bit masks per int32 element to handle duplicate indices.
+    aggregated: dict[int, int] = {}
+    for token_id in token_ids:
+        element_idx = token_id // 32
+        bit_idx = token_id % 32
+        aggregated[element_idx] = aggregated.get(element_idx, 0) | (1 << bit_idx)
+
+    row = vocab_mask[batch_idx]
+    element_indices = torch.tensor(
+        list(aggregated.keys()), dtype=torch.long, device=row.device
+    )
+    bitmasks = torch.tensor(
+        [
+            ctypes.c_int32(mask if is_allowed else ~mask).value
+            for mask in aggregated.values()
+        ],
+        dtype=row.dtype,
+        device=row.device,
+    )
+
+    if is_allowed:
+        row[element_indices] = torch.bitwise_or(row[element_indices], bitmasks)
+    else:
+        row[element_indices] = torch.bitwise_and(row[element_indices], bitmasks)